The present invention relates generally to agricultural implements such as combines and, more specifically, to automatic control of adjustments on such implements.
A modern agricultural harvester such as a combine is essentially a factory operating in the field with many interacting and complex adjustments to accommodate continually changing crop, field and machine conditions during harvest. Limited and often imprecise measurements make proper set-up and adjustment of the machine very difficult. Losses from improperly adjusted combines can be substantial, and the quality of the adjustments depends on the skill of the operator. Because the operator usually has to stop the combine, making the necessary adjustments is time-consuming and sometimes ignored so that productivity is compromised.
Despite many years of attempts to control the harvesters automatically, input from skilled operators having much accumulated knowledge is essential for proper adjustment and control of the machines. The operator knowledge is often in a form that cannot be incorporated into conventional control systems.
Examples of previous harvester control systems include those with look-up tables stored in an on-board memory, such as shown and described in U.S. Pat. No. 6,205,384. With such systems, current conditions as a group are compared to groups stored in memory. When current conditions as a group match a stored group (such as high, normal and low), the stored machine settings corresponding to the conditions are used to adjust the machine. New settings can be input by an operator via keyboard. One of the problems with this approach is basically that it is an open-loop approach. Machine settings are determined by historical data stored in the look-up table rather than by control results. As a result, such an open-loop type of system provides no compensation for changes in machine, crop, fields and environments.
Another example of harvester adjustment is shown and described in U.S. Pat. No. 5,586,033 wherein the control system trains a neural network model of the harvester with data. The model is then used to determine harvester settings. Neural nets in large size, however, require a prohibitive computational effort. At the current developmental stage of neural network techniques, large neural nets have limited practical use in harvester applications.
Numerous other harvester adjustment methods and devices have been employed. However, most of the methods attempt to control subsystems of the harvesting process, such as threshing unit control and cleaning fan control, with traditional control approaches. These attempts have, for the most part, been unsuccessful in the marketplace because they fail to take into consideration interactions between the harvesting subsystems.
It is therefore an object of the present invention to provide an improved control system for an agricultural harvester. It is another object to provide such a system which overcomes most or all of the aforementioned problems.
It is another object of the present invention to provide an improved control system for an agricultural harvester which has the ability to learn and adapt to changing conditions. It is a further object to provide such a system which can compensate for hardware changes, component wear, and crop condition and environment variability.
It is yet another object of the present invention to provide an improved control system for a harvester which has the ability to learn and adapt and to incorporate new machine settings learned from new experience.
It is a further object of the invention to provide an improved learning system for agricultural implements which is particularly useful for applications such as combine control. It is another object to provide such a system having the learning advantages of neural networks but overcoming the limitations of neural networks including the limitation of the huge amount of computational effort required by such networks.
It is another object of the present invention to provide an improved control system for a harvester, which controls the entire machine or process rather than isolated subsystems.
It is a further object to provide an improved control system for a harvester, which can utilize human expert knowledge of the harvesting process.
The intelligent hybrid control system includes a supervisory controller which monitors the quality of the harvesting process, such as grain loss, dockage and grain damage, and, based on the measurements, determines setpoints for all critical functional elements of the harvester. The system also includes a set of conventional low level controllers, and an adaptive neuro-fuzzy inference system which learns and remembers harvest situations. The intelligent hybrid control system combines advantages of human expert knowledge, fuzzy logic and neural nets. The system is able to utilize human expert knowledge, which is invaluable in controlling the complex harvesting process; to work effectively with vague and imprecise information typically provided in a harvester environment; and to learn and adapt automatically to incorporate settings learned from new experience.
Using the system with a combine, all critical elements of the quality of the harvesting process are monitored and controlled. Adjustments to the threshing/separating and cleaning shoe subsystems are made on-the-go to compensate for changing harvest and crop conditions. By using fuzzy logic and neural networks, the control system has the ability to remember past harvest situations in a manner similar to that of a human operator.
The system eliminates the need for constant operator monitoring and regular adjustment and reduces operator fatigue. The machine can operate continuously at performance levels suited to the particular desires of the operator.
These and other objects, features and advantages of the invention will become apparent to one skilled in the art upon reading the following description in view of the drawings.